feat: split training targets by task type

README.md

@@ -36,6 +36,7 @@ tech-architecture/  技术架构报告与本地 Mermaid 资源
 - `ExperimentRecord`：加工参数、质量指标与记录时间。
 - SQLite 骨架：`materials` 与 `experiments` 两张核心表，支持外键约束与实验记录往返。
 - 训练数据接口：从外部数据平台的 `material_records` 表只读加载记录，并分离元信息、特征和目标。
+- 目标拆分接口：按分类/回归任务组织 `is_cut_through`、`etching_depth` 等实验结果。
 - 工程配置：`pyproject.toml` 统一依赖、pytest 与 Ruff 配置。
 
 后续将按架构报告逐步补齐 DoE 生成、CSV 交换、设备采集、建模管线和推理服务。

src/lmpm/training/targets.py

@@ -0,0 +1,81 @@
+from typing import Literal
+
+from pydantic import BaseModel
+
+from lmpm.training.dataset import TrainingDataset
+
+TargetTask = Literal["classification", "regression"]
+
+
+class TargetSpec(BaseModel):
+    """The learning-task type of one platform outcome field."""
+
+    name: str
+    task: TargetTask
+
+
+class TargetSplit(BaseModel):
+    """Outcome values grouped by compatible learning task."""
+
+    classification: dict[str, tuple[bool, ...]]
+    regression: dict[str, tuple[float, ...]]
+
+
+TARGET_SPECS = (
+    TargetSpec(name="is_cut_through", task="classification"),
+    TargetSpec(name="carbonized_edge_width", task="regression"),
+    TargetSpec(name="etching_depth", task="regression"),
+    TargetSpec(name="is_fire_smolder", task="classification"),
+    TargetSpec(name="pattern_clarity_score", task="regression"),
+    TargetSpec(name="presentation_balance_score", task="regression"),
+)
+
+
+def target_specs() -> tuple[TargetSpec, ...]:
+    """Return the stable mapping from platform outcomes to task types."""
+    return TARGET_SPECS
+
+
+def split_targets(dataset: TrainingDataset) -> TargetSplit:
+    """Split complete outcome columns by learning task.
+
+    Empty datasets are allowed so dataset profiling can run before data
+    collection starts. Any incomplete target value is an error because a
+    training row must provide every outcome it is being trained against.
+    """
+    classification: dict[str, list[bool]] = {}
+    regression: dict[str, list[float]] = {}
+
+    for spec in TARGET_SPECS:
+        values: list[bool | float] = []
+        for record in dataset.records:
+            if spec.name not in record.targets or record.targets[spec.name] is None:
+                raise ValueError(
+                    f"{record.metadata['experiment_id']}: missing target {spec.name}"
+                )
+
+            value = record.targets[spec.name]
+            if spec.task == "classification":
+                if not isinstance(value, bool):
+                    raise ValueError(
+                        f"{record.metadata['experiment_id']}: "
+                        f"{spec.name} must be boolean"
+                    )
+                values.append(value)
+            else:
+                if isinstance(value, bool) or not isinstance(value, (int, float)):
+                    raise ValueError(
+                        f"{record.metadata['experiment_id']}: "
+                        f"{spec.name} must be numeric"
+                    )
+                values.append(float(value))
+
+        if spec.task == "classification":
+            classification[spec.name] = values
+        else:
+            regression[spec.name] = values
+
+    return TargetSplit(
+        classification={name: tuple(values) for name, values in classification.items()},
+        regression={name: tuple(values) for name, values in regression.items()},
+    )

tests/test_training_targets.py

@@ -0,0 +1,82 @@
+import pytest
+from pydantic import ValidationError
+
+from lmpm.training.dataset import TrainingDataset, TrainingRecord
+from lmpm.training.targets import TargetSpec, split_targets, target_specs
+
+
+def make_record(number: int = 1, *, target_overrides=None) -> TrainingRecord:
+    targets = {
+        "is_cut_through": True,
+        "carbonized_edge_width": 0.2,
+        "etching_depth": 30.0,
+        "is_fire_smolder": False,
+        "pattern_clarity_score": 9.0,
+        "presentation_balance_score": 8.0,
+    }
+    if target_overrides:
+        targets.update(target_overrides)
+
+    return TrainingRecord(
+        metadata={"experiment_id": f"LAS-2026-{number:04d}"},
+        features={"actual_output_power": 8.5},
+        targets=targets,
+    )
+
+
+def test_target_specs_describe_all_platform_targets():
+    specs = target_specs()
+
+    assert [(spec.name, spec.task) for spec in specs] == [
+        ("is_cut_through", "classification"),
+        ("carbonized_edge_width", "regression"),
+        ("etching_depth", "regression"),
+        ("is_fire_smolder", "classification"),
+        ("pattern_clarity_score", "regression"),
+        ("presentation_balance_score", "regression"),
+    ]
+
+
+def test_target_spec_rejects_unknown_task_type():
+    with pytest.raises(ValidationError):
+        TargetSpec(name="unsupported_target", task="unknown")
+
+
+def test_split_targets_groups_by_task_type():
+    dataset = TrainingDataset(
+        records=(
+            make_record(1),
+            make_record(2, target_overrides={"is_cut_through": False}),
+        )
+    )
+
+    split = split_targets(dataset)
+
+    assert split.classification["is_cut_through"] == (True, False)
+    assert split.classification["is_fire_smolder"] == (False, False)
+    assert split.regression["carbonized_edge_width"] == pytest.approx((0.2, 0.2))
+    assert split.regression["etching_depth"] == pytest.approx((30.0, 30.0))
+    assert split.regression["pattern_clarity_score"] == pytest.approx((9.0, 9.0))
+    assert split.regression["presentation_balance_score"] == pytest.approx((8.0, 8.0))
+
+
+def test_empty_dataset_has_empty_target_groups():
+    split = split_targets(TrainingDataset(records=()))
+
+    assert split.classification == {"is_cut_through": (), "is_fire_smolder": ()}
+    assert split.regression == {
+        "carbonized_edge_width": (),
+        "etching_depth": (),
+        "pattern_clarity_score": (),
+        "presentation_balance_score": (),
+    }
+
+
+@pytest.mark.parametrize("target_name", ["is_cut_through", "etching_depth"])
+def test_missing_target_value_is_rejected(target_name):
+    dataset = TrainingDataset(
+        records=(make_record(target_overrides={target_name: None}),)
+    )
+
+    with pytest.raises(ValueError, match=target_name):
+        split_targets(dataset)